Project Participants: Queensland University of Technology (QUT) Central Queensland University (CQU) Monash University (MU) University of Wollongong (UOW) Industrial Partners: V/Line Department of Transport Victoria Rio Tinto ARTC & KiwiRail

Outline of the Presentation An overview Common weaknesses of existing BMS in Australia Maintenance optimisation process – summary Framework of the proposed BMS Classification (or Categorisation) of network of bridges Prediction of Remaining Service Potential (RSP) Durability Assessment of Steel Bridges: Failure Due to Corrosion and Cracking Criticality and Vulnerability Analysis Synthetics Rating Maintenance optimisation

There are over 9,480 bridges in the major Australian Rail Networks: – 3,710 in Queensland Rail (including QRN); – 3,230 in ARTC; – 1,200 in RailCorp; – 990 in V/Line; – 350 in TasRail and – 40 in Rio Tinto Over 30% of these bridges are over 80 years old Replacement of 3000 bridges nationally at a cost of $4.5 Billion over 20 years An Overview

Common weaknesses of existing BMS in Australia Syndromes and symptoms Bridge classification (or categorisation) is generic Inspection records are not detail enough for maintenance optimisation at network level Deterioration models are not in use and remaining service potential cannot be predicted Maintenance intervention points cannot be identified Maintenance strategies cannot be compared (eg. Repair work, Strengthening) Subjective maintenance work based on human judgements Item vice cost cannot be identified and maintenance cost cannot be optimised

Maintenance Optimisation Process - Summary Future conditions of the components (UOW and MU) Rating based on structural Criticality and Vulnerability analysis (QUT) Rate Bridges based on current and future conditions (Synthetic rating) Remaining life + Intervention frequencies Current conditions of the components from inspection Alternative management strategies MR&R optimisation Work orders QUT UOW+MU+QUT CQU

Phase 1 Framework of the Proposed BMS Inspection module Synthetic rating module Bridge Inventory Data Environmental classification Deterioration modelling Bridge Classification Loading QUTUOW+MU Future Condition Assessment (Prediction) Current Condition Assessment Intervention frequencies Maintenance History QUT+ UOW+MU QUT Future condition of components Remaining Service Potential (RSP) of components Rating based Criticality and Vulnerability Flood, Wind, Earthquake Vehicle collision, Environmental effects

Phase 2 Framework of the proposed BMS (cont) Maintenance quality or political decisions Unacceptable Budget limits Project level optimization Network level optimization (Network level criticality) Component interaction Analysis period, analysis scenarios and base case Define alternative bridge management strategies ( Preventative maintenance, Repair work, Strengthening, Replacement, Do Nothing) Calculate Net Present Value Minor works or Regular repair Estimate costs Agency & routine maintenance User, work related, other Vulnerability cost Modify management strategies MR&R optimization module Assignment of projects to work groups Prepare work bids and plains Select preferred strategy Record maintenance history Maintenance implementation Performance review CQU

Classification (or Categorisation) of network of bridges

Prediction of Remaining Service Potential (UOW) Contributing factors : Rail-traffic volume (Tonnage ) Number of tracks, Material type, Functional class, Nature of the defect Structure type Environmental categories, etc. Markov chain based stochastic deterioration models were selected Regression-based nonlinear optimization techniques were use to estimate the Transition Probability Matrixes (TPM). Deterioration curves were developed for classified element groups based on their; Structural role Maintenance requirements Costing or inspection procedures Environmental category Traffic volume

(a) Network level Analysis Results By using one TPM A typical example for a TPM of a primary beam (Average Performance Index vs Age) ( b) Network level Analysis By using multiple TPMs(c) Application of Markov approach for approximate service life prediction of single components

Highlights Expected performance index curves and transition probability derived for entire life of a subcomponent can be used to comparison purpose and network level bridge management decisions. Markov approach can be used to predict the average remaining service life estimation of individual components after considering non- homogeneity of the deterioration process, by considering separate Transition Probability for different time zones. Inspection intervals need to be predicted by rating each important element of these components. Accuracy of the service life estimation is depend on the reliability of the data. Transition Probability matrixes should be updated when new data available in the future.

Remaining Service Potential of Steel Bridges (MU): (Failure Due to Corrosion and Cracking) The engineering assessment of the durability requires a knowledge of both the operational usage and the environment (rate of corrosion). Monitoring Corrosion on Bridge 44 Material behavior from 7 microns upwards can be represented as: REPOS measured for Three Classes of Trains

Criticality and Vulnerability Analysis (QUT) Scope: Setup of Criticality and Vulnerability Rating Criteria: The factors related to the Structural Condition are taken into account. Bridges will be rated based on Synthetic Rating Method. Critical factors: Live Load Environment factors such as corrosion and temperature Extreme events such as Flood, Wind, Earthquake & Collusion The vulnerability may refer to the vulnerability of whole structure or vulnerability of the critical elements of the structure. The degree of the criticality of the structural elements is identified by weighting factors Criticality of the elements due to different structural configuration Criticality of the factors according to the environmental condition

Synthetics Rating (QUT) (1)Condition rating (Inspection+ RSP) (2) Criticality and Vulnerability analysis Current condition of the bridge: Future condition of the bridge: FactorCurrentFuture Flood Wind Earthquake Collision0.0 Environment (3) Vulnerability rating of each bridge (4) Synthetic rating of each bridge

Maintenance optimisation (CQU)

Priority Order Element Criticality Defect Severity Network Criticality 145 Use Highest % first Maintenance optimisation... Priority ranking Risk Priority Number Probability of FailureConsequences of Failure SafetyEnvironmentFunctionalitySustainability Element criticalityNetwork criticalityInspection cost (to reduce the risk) Maintenance/ repair cost Bridge element criticality rating Criticality Rating Description 1Non-structural 2Structural with redundancy 3Protective 4Structural without redundancy Network Criticality Repair priority ranking

Proposed Software Platform

Acknowledgement To our Industrial partners including V/Line, Rio Tinto and ARTC for their generous support. V/line– North East corridor